Student in Nonlinear Optimization

In the context of context of communication networks, the solving of bound-constrained nonlinear optimization problems with, e.g., the Newton method or the Augmented Lagrangian Method (ALM), require the evaluation of the objective function, its gradient and the (sparsity pattern of the) Hessian matrix. Additionally, constrained optimization problems also involve providing the sparsity pattern and the Jacobian matrix of the constraints. The time and resource required to obtain this information and verify their correctness can be relatively large even for simple problems. Nowadays, optimization problem-solving environments provide modeling language and state-of-the-art optimization solvers together with packages that are capable to compute first-order information, e.g., derivatives, gradients. In this context, Automatic Differentiation (AD) remains largely unexploited for generating these quantities, requiring in turn to run the entire framework and its packages altogether to solve nonlinear optimization problems. Thus, it would also be beneficial to solve the corresponding nonconvex optimization problem (including nonlinear inequality constraints) by means of unified AD tools. Indeed, combining AD tools that automatically generate the required first- and second-order quantities together with nonlinear optimization methods such as ALM yields a promising method.

Objective: investigate and design nonlinear programming methods for solving nonconvex optimization problems with nonlinear constraints that can rapidly converge with few function and derivative evaluations. Ideally the proposed algorithm would also improve second-order information with the same efficiency and reliability as available for first-order information.

Task(s):

  • Formulate, develop and numerically evaluate enhancements/extensions of computational methods/algorithms including ALM for solving nonconvex optimization problems with nonlinear constraints.
  • To address the computational performance objectives, the candidate will also actively participate to the design (extension) and evaluation of (existing) automatic differentiation tools for solving such optimization problems.
  • Integration of these tools into a unified AD framework will be realized in cooperation with computational/numerical method experts. These tasks will be realized under the supervision of a senior (postdoc-level) researcher.

Duration:

  • Short duration (from 3 to 6 months): suited for MSc curriculum course
  • Long duration (up to 12 months): suited for MSc thesis internship

Candidate profile: MSc (or last year of MSc curriculum) in applied mathematics, mathematical engineering, theoretical computer science, or computer science engineering

  • Continuous optimization and related nonlinear programming techniques such as first-order iterative methods (PGD/PPD, ALM, ADM, etc.), inexact methods (Quasi-Newton methods, BFGS, etc.) and/or Krylov subspace methods ((B)CG, Lanczos, GMRES, MINRES, Arnoldi, etc.) for nonconvex optimization problems.
  • Experience in programming with nonlinear optimization libraries, e.g., LANCELOT/ CUTE, MINOS, TRON, IPopt, NLopt, etc. is considered as a strong plus.
  • Excellent written, verbal and interpersonal communication skills.

Application requirements

  • Certified copy of the MSc diploma/certificate shall be included in annex of the CV.
  • The CV shall indicate the detailed coordinates of the current or eventually the last academic institution and department of the candidate.
  • The CV shall include a detailed list of publications/achievements in relation to the job description.
  • Note well: the candidate must follow or have obtained his/her MSc degree from an academic institution of one of the EU countries.

Starting date: Jan.1st, 2023 (earliest) – Sep.30, 2023 (latest).

Su solicitud de empleo será evaluada por el departamento de RR.HH. de Huawei R&D Sites in Belgium and the Netherlands . Para cualquier información adicional relativa a su solicitud, le remitimos al departamento de RR.HH. Huawei R&D Sites in Belgium and the Netherlands.

Huawei R&D Sites in Belgium and the Netherlands

Huawei is a leading telecom solutions provider. Through continuous customer-centric innovation, Huawei has established end-to-end advantages in Telecom Network Infrastructure, Application & Software, Professional Services and Devices. With comprehensive strengths in wireline, wireless and IP technologies, Huawei has gained a leading position in the All-IP convergence age. Its products and solutions have been deployed in over 100 countries and have served 45 of the world's top 50 telecom operators, as well as one third of the world's population.